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Record W2168430622 · doi:10.1158/1078-0432.ccr-03-0165

Serum Macrophage Inhibitory Cytokine 1 as a Marker of Pancreatic and Other Periampullary Cancers

2004· article· en· W2168430622 on OpenAlexaff
Jens Koopmann, Phillip Buckhaults, David A. Brown, Marianna Zahurak, Norihiro Sato, Noriyoshi Fukushima, Lori J. Sokoll, Daniel W. Chan, Charles J. Yeo, Ralph H. Hruban, Samuel N. Breit, Kenneth W. Kinzler, Bert Vogelstein, Michael Goggins

Bibliographic record

VenueClinical Cancer Research · 2004
Typearticle
Languageen
FieldMedicine
TopicGDF15 and Related Biomarkers
Canadian institutionsImmunovaccine (Canada)
FundersNational Cancer Institute
KeywordsPancreatic cancerMedicinePancreatic diseaseAdenocarcinomaPathologyCA19-9Tissue microarrayImmunohistochemistryPancreasCancerPancreatitisInternal medicineCancer research

Abstract

fetched live from OpenAlex

PURPOSE: Patients with pancreatic ductal adenocarcinoma usually present with advanced-stage disease and a dismal prognosis. One effective strategy likely to improve the morbidity and mortality from pancreatic cancer would be the identification of accurate, noninvasive diagnostic markers that would enable earlier diagnosis of symptomatic patients and earlier detection of cancer in asymptomatic individuals at high risk for developing pancreatic cancer. In this study, we evaluated serum macrophage inhibitory cytokine-1 (MIC-1) as a marker of pancreatic cancer. EXPERIMENTAL DESIGN: MIC-1 expression in primary pancreatic cancers, intraductal papillary mucinous neoplasms, and pancreatic cancer cell lines was determined using the National Center for Biotechnology Information serial analysis of gene expression database, oligonucleotide microarrays analysis, in situ hybridization, and immunohistochemistry. Serum MIC-1 levels were determined by ELISA in 80 patients with pancreatic adenocarcinomas, in 30 patients with ampullary and cholangiocellular carcinomas, in 42 patients with benign pancreatic tumors, in 76 patients with chronic pancreatitis, and in 97 healthy control subjects. The diagnostic performance of serum MIC-1 as a marker of pancreatic cancer was compared with that of serum CA19-9. RESULTS: Oligonucleotide microarray and serial analysis of gene expression data demonstrated that MIC-1 RNA levels were higher in primary pancreatic cancers, intraductal papillary mucinous neoplasms, and pancreatic cancer cell lines than in nonneoplastic pancreatic ductal epithelium. MIC-1 expression was localized to the malignant epithelium in pancreatic adenocarcinomas by in situ hybridization. MIC-1 protein was expressed in 14 of 16 primary pancreatic adenocarcinomas (88%) by immunohistochemistry and was also expressed in some pancreata affected by pancreatitis but not in normal pancreas. Serum MIC-1 levels were significantly higher in patients with pancreatic ductal adenocarcinoma (mean +/- SD, 2428 +/- 2324 pg/ml) and in patients with ampullary and cholangiocellular carcinomas (2123 +/- 2387 pg/ml) than in those with benign pancreatic neoplasms (940 +/- 469 pg/ml), chronic pancreatitis (1364 +/- 1236 pg/ml), or in healthy controls (546 +/- 262 pg/ml). An elevated serum MIC-1 (defined as 2 SD above the mean for healthy controls) performed as well as CA19-9 (area under the receiver operating characteristic curve, 0.81 and 0.77, respectively), and the combination of MIC-1 and CA19-9 significantly improved diagnostic accuracy (P < 0.05; area under the receiver operating characteristic curve, 0.87; sensitivity, 70%; specificity, 85%). CONCLUSION: Serum MIC-1 measurement can aid in the diagnosis of pancreatic adenocarcinoma.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.103
GPT teacher head0.477
Teacher spread0.374 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations277
Published2004
Admission routes1
Has abstractyes

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